Overview

Dataset statistics

Number of variables16
Number of observations1325100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory204.1 MiB
Average record size in memory161.5 B

Variable types

Categorical4
DateTime1
Numeric11

Alerts

cusip_id has a high cardinality: 1893 distinct valuesHigh cardinality
bond_sym_id has a high cardinality: 1981 distinct valuesHigh cardinality
company_symbol has a high cardinality: 520 distinct valuesHigh cardinality
rptd_pr is highly overall correlated with yld_ptHigh correlation
yld_pt is highly overall correlated with rptd_prHigh correlation
offering_yield is highly overall correlated with couponHigh correlation
coupon is highly overall correlated with offering_yieldHigh correlation
offering_month is highly overall correlated with maturity_monthHigh correlation
maturity_month is highly overall correlated with offering_monthHigh correlation
principal_amt is highly imbalanced (98.5%)Imbalance
yld_pt is highly skewed (γ1 = 175.5750013)Skewed
offering_yield is highly skewed (γ1 = 43.47418549)Skewed
cusip_id is uniformly distributedUniform

Reproduction

Analysis started2023-08-10 18:42:53.548349
Analysis finished2023-08-10 18:43:36.685457
Duration43.14 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

cusip_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1893
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
00037BAB8
 
700
65339KBM1
 
700
655844BJ6
 
700
655664AT7
 
700
655664AS9
 
700
Other values (1888)
1321600 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters11925900
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00037BAB8
2nd row00037BAB8
3rd row00037BAB8
4th row00037BAB8
5th row00037BAB8

Common Values

ValueCountFrequency (%)
00037BAB8 700
 
0.1%
65339KBM1 700
 
0.1%
655844BJ6 700
 
0.1%
655664AT7 700
 
0.1%
655664AS9 700
 
0.1%
65504LAP2 700
 
0.1%
65504LAN7 700
 
0.1%
655044AP0 700
 
0.1%
655044AH8 700
 
0.1%
654902AE5 700
 
0.1%
Other values (1883) 1318100
99.5%

Length

2023-08-10T18:43:37.331201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00037bab8 700
 
0.1%
00206rcl4 700
 
0.1%
001055ap7 700
 
0.1%
001055aq5 700
 
0.1%
001546at7 700
 
0.1%
00164vac7 700
 
0.1%
00164vae3 700
 
0.1%
00185aad6 700
 
0.1%
00185aaf1 700
 
0.1%
00185aak0 700
 
0.1%
Other values (1883) 1318100
99.5%

Most occurring characters

ValueCountFrequency (%)
0 1026900
 
8.6%
2 940800
 
7.9%
7 891100
 
7.5%
4 889000
 
7.5%
6 870100
 
7.3%
3 866600
 
7.3%
1 864500
 
7.2%
5 856800
 
7.2%
A 845600
 
7.1%
8 833700
 
7.0%
Other values (24) 3040800
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8731100
73.2%
Uppercase Letter 3194800
 
26.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 845600
26.5%
B 355600
 
11.1%
C 208600
 
6.5%
D 151200
 
4.7%
E 112700
 
3.5%
H 103600
 
3.2%
F 100100
 
3.1%
N 100100
 
3.1%
X 98000
 
3.1%
J 97300
 
3.0%
Other values (14) 1022000
32.0%
Decimal Number
ValueCountFrequency (%)
0 1026900
11.8%
2 940800
10.8%
7 891100
10.2%
4 889000
10.2%
6 870100
10.0%
3 866600
9.9%
1 864500
9.9%
5 856800
9.8%
8 833700
9.5%
9 691600
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 8731100
73.2%
Latin 3194800
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 845600
26.5%
B 355600
 
11.1%
C 208600
 
6.5%
D 151200
 
4.7%
E 112700
 
3.5%
H 103600
 
3.2%
F 100100
 
3.1%
N 100100
 
3.1%
X 98000
 
3.1%
J 97300
 
3.0%
Other values (14) 1022000
32.0%
Common
ValueCountFrequency (%)
0 1026900
11.8%
2 940800
10.8%
7 891100
10.2%
4 889000
10.2%
6 870100
10.0%
3 866600
9.9%
1 864500
9.9%
5 856800
9.8%
8 833700
9.5%
9 691600
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11925900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1026900
 
8.6%
2 940800
 
7.9%
7 891100
 
7.5%
4 889000
 
7.5%
6 870100
 
7.3%
3 866600
 
7.3%
1 864500
 
7.2%
5 856800
 
7.2%
A 845600
 
7.1%
8 833700
 
7.0%
Other values (24) 3040800
25.5%
Distinct1577
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
Minimum2017-01-04 00:00:00
Maximum2022-09-01 00:00:00
2023-08-10T18:43:37.444777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:37.811426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

bond_sym_id
Categorical

Distinct1981
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
ABB3852125
 
700
MUR3856177
 
700
NWL3865872
 
700
NTAP4547207
 
700
NTAP4131791
 
700
Other values (1976)
1321600 

Length

Max length12
Median length10
Mean length9.9765542
Min length8

Characters and Unicode

Total characters13219932
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABB3852125
2nd rowABB3852125
3rd rowABB3852125
4th rowABB3852125
5th rowABB3852125

Common Values

ValueCountFrequency (%)
ABB3852125 700
 
0.1%
MUR3856177 700
 
0.1%
NWL3865872 700
 
0.1%
NTAP4547207 700
 
0.1%
NTAP4131791 700
 
0.1%
NTAP3940743 700
 
0.1%
CMCS3910950 700
 
0.1%
NFG4543079 700
 
0.1%
MYL4072753 700
 
0.1%
MUR4880676 700
 
0.1%
Other values (1971) 1318100
99.5%

Length

2023-08-10T18:43:37.927272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abb3852125 700
 
0.1%
are4459169 700
 
0.1%
acc4136081 700
 
0.1%
aep4563780 700
 
0.1%
aep4773531 700
 
0.1%
ael4507378 700
 
0.1%
axp4633129 700
 
0.1%
axp4662255 700
 
0.1%
axp4764026 700
 
0.1%
axp4764027 700
 
0.1%
Other values (1971) 1318100
99.5%

Most occurring characters

ValueCountFrequency (%)
4 2066400
15.6%
3 993300
 
7.5%
8 844200
 
6.4%
6 840000
 
6.4%
0 786800
 
6.0%
2 778400
 
5.9%
5 768600
 
5.8%
1 758800
 
5.7%
9 755300
 
5.7%
7 683900
 
5.2%
Other values (26) 3944232
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9275700
70.2%
Uppercase Letter 3944232
29.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 321354
 
8.1%
A 303906
 
7.7%
M 284451
 
7.2%
S 259563
 
6.6%
P 234284
 
5.9%
B 213910
 
5.4%
T 213324
 
5.4%
L 198984
 
5.0%
N 175470
 
4.4%
E 163368
 
4.1%
Other values (16) 1575618
39.9%
Decimal Number
ValueCountFrequency (%)
4 2066400
22.3%
3 993300
10.7%
8 844200
9.1%
6 840000
9.1%
0 786800
 
8.5%
2 778400
 
8.4%
5 768600
 
8.3%
1 758800
 
8.2%
9 755300
 
8.1%
7 683900
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9275700
70.2%
Latin 3944232
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 321354
 
8.1%
A 303906
 
7.7%
M 284451
 
7.2%
S 259563
 
6.6%
P 234284
 
5.9%
B 213910
 
5.4%
T 213324
 
5.4%
L 198984
 
5.0%
N 175470
 
4.4%
E 163368
 
4.1%
Other values (16) 1575618
39.9%
Common
ValueCountFrequency (%)
4 2066400
22.3%
3 993300
10.7%
8 844200
9.1%
6 840000
9.1%
0 786800
 
8.5%
2 778400
 
8.4%
5 768600
 
8.3%
1 758800
 
8.2%
9 755300
 
8.1%
7 683900
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13219932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2066400
15.6%
3 993300
 
7.5%
8 844200
 
6.4%
6 840000
 
6.4%
0 786800
 
6.0%
2 778400
 
5.9%
5 768600
 
5.8%
1 758800
 
5.7%
9 755300
 
5.7%
7 683900
 
5.2%
Other values (26) 3944232
29.8%

company_symbol
Categorical

Distinct520
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
AAPL
 
18900
C
 
16100
CMCS
 
16100
GM
 
16100
DUK
 
14700
Other values (515)
1243200 

Length

Max length5
Median length3
Mean length2.9765542
Min length1

Characters and Unicode

Total characters3944232
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABB
2nd rowABB
3rd rowABB
4th rowABB
5th rowABB

Common Values

ValueCountFrequency (%)
AAPL 18900
 
1.4%
C 16100
 
1.2%
CMCS 16100
 
1.2%
GM 16100
 
1.2%
DUK 14700
 
1.1%
BA 14700
 
1.1%
BP 14000
 
1.1%
CVS 14000
 
1.1%
F 12600
 
1.0%
UNH 12600
 
1.0%
Other values (510) 1175300
88.7%

Length

2023-08-10T18:43:38.034857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aapl 18900
 
1.4%
c 16100
 
1.2%
cmcs 16100
 
1.2%
gm 16100
 
1.2%
duk 14700
 
1.1%
ba 14700
 
1.1%
bp 14000
 
1.1%
cvs 14000
 
1.1%
f 12600
 
1.0%
unh 12600
 
1.0%
Other values (510) 1175300
88.7%

Most occurring characters

ValueCountFrequency (%)
C 321354
 
8.1%
A 303906
 
7.7%
M 284451
 
7.2%
S 259563
 
6.6%
P 234284
 
5.9%
B 213910
 
5.4%
T 213324
 
5.4%
L 198984
 
5.0%
N 175470
 
4.4%
E 163368
 
4.1%
Other values (16) 1575618
39.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3944232
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 321354
 
8.1%
A 303906
 
7.7%
M 284451
 
7.2%
S 259563
 
6.6%
P 234284
 
5.9%
B 213910
 
5.4%
T 213324
 
5.4%
L 198984
 
5.0%
N 175470
 
4.4%
E 163368
 
4.1%
Other values (16) 1575618
39.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3944232
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 321354
 
8.1%
A 303906
 
7.7%
M 284451
 
7.2%
S 259563
 
6.6%
P 234284
 
5.9%
B 213910
 
5.4%
T 213324
 
5.4%
L 198984
 
5.0%
N 175470
 
4.4%
E 163368
 
4.1%
Other values (16) 1575618
39.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3944232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 321354
 
8.1%
A 303906
 
7.7%
M 284451
 
7.2%
S 259563
 
6.6%
P 234284
 
5.9%
B 213910
 
5.4%
T 213324
 
5.4%
L 198984
 
5.0%
N 175470
 
4.4%
E 163368
 
4.1%
Other values (16) 1575618
39.9%

entrd_vol_qt
Real number (ℝ)

Distinct18633
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1356446.6
Minimum1
Maximum5.83931 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:38.149378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10000
Q154000
median270000
Q31011000
95-th percentile6000000
Maximum5.83931 × 108
Range5.83931 × 108
Interquartile range (IQR)957000

Descriptive statistics

Standard deviation3515981.9
Coefficient of variation (CV)2.5920532
Kurtosis799.35082
Mean1356446.6
Median Absolute Deviation (MAD)245000
Skewness13.065426
Sum1.7974275 × 1012
Variance1.2362129 × 1013
MonotonicityNot monotonic
2023-08-10T18:43:38.264211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 55837
 
4.2%
500000 55613
 
4.2%
1000000 51678
 
3.9%
50000 47090
 
3.6%
200000 39602
 
3.0%
25000 37691
 
2.8%
10000 33883
 
2.6%
2000000 30062
 
2.3%
250000 29652
 
2.2%
20000 24642
 
1.9%
Other values (18623) 919350
69.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
500 2
 
< 0.1%
1000 1022
 
0.1%
2000 8291
0.6%
2286 1
 
< 0.1%
3000 7352
 
0.6%
4000 6258
 
0.5%
5000 18526
1.4%
6000 5729
 
0.4%
7000 4736
 
0.4%
ValueCountFrequency (%)
583931000 1
< 0.1%
265568000 1
< 0.1%
237000000 1
< 0.1%
223650000 1
< 0.1%
201825000 1
< 0.1%
201139000 1
< 0.1%
193200000 1
< 0.1%
182000000 1
< 0.1%
176695000 1
< 0.1%
173500000 1
< 0.1%

rptd_pr
Real number (ℝ)

Distinct126514
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.32245
Minimum1 × 10-6
Maximum142.646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:38.383625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-6
5-th percentile95.576
Q1100.425
median103.715
Q3108.304
95-th percentile115.24705
Maximum142.646
Range142.646
Interquartile range (IQR)7.879

Descriptive statistics

Standard deviation7.116482
Coefficient of variation (CV)0.068216205
Kurtosis15.901142
Mean104.32245
Median Absolute Deviation (MAD)3.708
Skewness-1.3255251
Sum1.3823768 × 108
Variance50.644316
MonotonicityNot monotonic
2023-08-10T18:43:38.498346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1721
 
0.1%
101 1188
 
0.1%
102 1065
 
0.1%
100.5 994
 
0.1%
101.5 970
 
0.1%
99 954
 
0.1%
103 912
 
0.1%
99.5 901
 
0.1%
104 894
 
0.1%
101.25 892
 
0.1%
Other values (126504) 1314609
99.2%
ValueCountFrequency (%)
1 × 10-61
 
< 0.1%
0.625 1
 
< 0.1%
1 4
< 0.1%
1.1 1
 
< 0.1%
1.125 1
 
< 0.1%
1.151 1
 
< 0.1%
1.188 1
 
< 0.1%
1.25 1
 
< 0.1%
1.375 1
 
< 0.1%
1.47 1
 
< 0.1%
ValueCountFrequency (%)
142.646 1
< 0.1%
142.29 1
< 0.1%
142.275 1
< 0.1%
142.044 1
< 0.1%
141.998 1
< 0.1%
141.897 1
< 0.1%
141.846 1
< 0.1%
141.813 1
< 0.1%
141.79 1
< 0.1%
141.75 1
< 0.1%

yld_pt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1100043
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5137435
Minimum0
Maximum2292.1058
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:38.620934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.36041385
Q11.2484038
median2.2548945
Q33.3019775
95-th percentile5.014431
Maximum2292.1058
Range2292.1058
Interquartile range (IQR)2.0535737

Descriptive statistics

Standard deviation4.2567184
Coefficient of variation (CV)1.6933782
Kurtosis71345.963
Mean2.5137435
Median Absolute Deviation (MAD)1.027356
Skewness175.575
Sum3330961.5
Variance18.119652
MonotonicityNot monotonic
2023-08-10T18:43:38.734180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 14
 
< 0.1%
3 13
 
< 0.1%
0.15 11
 
< 0.1%
0.100001 10
 
< 0.1%
0.2 9
 
< 0.1%
0.953921 8
 
< 0.1%
0.6 8
 
< 0.1%
1.862988 8
 
< 0.1%
0.23 7
 
< 0.1%
2.332995 7
 
< 0.1%
Other values (1100033) 1325005
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.000295 1
< 0.1%
0.000297 1
< 0.1%
0.000306 1
< 0.1%
0.000394 1
< 0.1%
0.000407 1
< 0.1%
0.000516 1
< 0.1%
0.000724 1
< 0.1%
0.000779 1
< 0.1%
0.000798 1
< 0.1%
ValueCountFrequency (%)
2292.105824 1
< 0.1%
1103.000191 1
< 0.1%
786.91838 1
< 0.1%
686.986965 1
< 0.1%
653.930338 1
< 0.1%
602.287567 1
< 0.1%
586.555133 1
< 0.1%
537.639051 1
< 0.1%
530.554969 1
< 0.1%
511.780674 1
< 0.1%

offering_amt
Real number (ℝ)

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987463.81
Minimum225000
Maximum11000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:38.850441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum225000
5-th percentile300000
Q1500000
median750000
Q31250000
95-th percentile2250000
Maximum11000000
Range10775000
Interquartile range (IQR)750000

Descriptive statistics

Standard deviation782226.15
Coefficient of variation (CV)0.79215678
Kurtosis40.546021
Mean987463.81
Median Absolute Deviation (MAD)250000
Skewness4.5932382
Sum1.3084883 × 1012
Variance6.1187775 × 1011
MonotonicityNot monotonic
2023-08-10T18:43:38.969917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 188300
14.2%
1000000 184100
13.9%
750000 147000
 
11.1%
1500000 84000
 
6.3%
1250000 81900
 
6.2%
600000 74200
 
5.6%
400000 65800
 
5.0%
300000 46900
 
3.5%
700000 42000
 
3.2%
2000000 40600
 
3.1%
Other values (63) 370300
27.9%
ValueCountFrequency (%)
225000 700
 
0.1%
250000 19600
 
1.5%
275000 2800
 
0.2%
300000 46900
3.5%
325000 1400
 
0.1%
350000 37800
2.9%
375000 2100
 
0.2%
380000 700
 
0.1%
400000 65800
5.0%
420000 700
 
0.1%
ValueCountFrequency (%)
11000000 1400
0.1%
9000000 700
 
0.1%
7500000 700
 
0.1%
6000000 2100
0.2%
5500000 700
 
0.1%
5000000 2100
0.2%
4250000 1400
0.1%
4000000 2800
0.2%
3750000 700
 
0.1%
3500000 2100
0.2%

offering_price
Real number (ℝ)

Distinct748
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.684888
Minimum95.273
Maximum106.94884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:39.088210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum95.273
5-th percentile98.954
Q199.59
median99.808
Q399.937
95-th percentile100
Maximum106.94884
Range11.67584
Interquartile range (IQR)0.347

Descriptive statistics

Standard deviation0.43945333
Coefficient of variation (CV)0.0044084248
Kurtosis55.129404
Mean99.684888
Median Absolute Deviation (MAD)0.155
Skewness-0.36003979
Sum1.3209245 × 108
Variance0.19311923
MonotonicityNot monotonic
2023-08-10T18:43:39.208000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 158900
 
12.0%
99.975 7700
 
0.6%
99.992 7000
 
0.5%
99.888 6300
 
0.5%
99.83 6300
 
0.5%
99.819 6300
 
0.5%
99.899 5600
 
0.4%
99.861 5600
 
0.4%
99.983 5600
 
0.4%
99.957 5600
 
0.4%
Other values (738) 1110200
83.8%
ValueCountFrequency (%)
95.273 700
0.1%
95.291 700
0.1%
95.83 700
0.1%
97.052 700
0.1%
97.541 700
0.1%
97.589 700
0.1%
97.636 700
0.1%
97.698 700
0.1%
97.818 700
0.1%
97.831 700
0.1%
ValueCountFrequency (%)
106.94884 700
 
0.1%
100.63342 700
 
0.1%
100.5 700
 
0.1%
100 158900
12.0%
99.999 1400
 
0.1%
99.998 1400
 
0.1%
99.997 2800
 
0.2%
99.996 700
 
0.1%
99.995 4200
 
0.3%
99.994 4200
 
0.3%

offering_yield
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1765
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean928.11627
Minimum1.57296
Maximum1750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:39.325187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.57296
5-th percentile2.26303
Q12.98399
median3.563
Q34.17621
95-th percentile5.4
Maximum1750000
Range1749998.4
Interquartile range (IQR)1.19222

Descriptive statistics

Standard deviation40211.221
Coefficient of variation (CV)43.325629
Kurtosis1888.0077
Mean928.11627
Median Absolute Deviation (MAD)0.588
Skewness43.474185
Sum1.2298469 × 109
Variance1.6169423 × 109
MonotonicityNot monotonic
2023-08-10T18:43:39.441915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.375 6300
 
0.5%
5.25 6300
 
0.5%
5 4900
 
0.4%
5.625 4900
 
0.4%
5.125 4900
 
0.4%
6.125 4200
 
0.3%
4.75 4200
 
0.3%
5.75 3500
 
0.3%
5.875 3500
 
0.3%
4.375 2100
 
0.2%
Other values (1755) 1280300
96.6%
ValueCountFrequency (%)
1.57296 700
0.1%
1.57902 700
0.1%
1.64392 700
0.1%
1.65 700
0.1%
1.695 700
0.1%
1.70503 700
0.1%
1.72201 700
0.1%
1.724 700
0.1%
1.728 700
0.1%
1.73404 700
0.1%
ValueCountFrequency (%)
1750000 700
 
0.1%
9 700
 
0.1%
8.62493 700
 
0.1%
8.125 700
 
0.1%
7.75 1400
0.1%
7.7494 700
 
0.1%
7.625 2100
0.2%
7.5 2100
0.2%
7.375 1400
0.1%
7.25 700
 
0.1%

principal_amt
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
1000.0
1322300 
200000.0
 
2100
0.0
 
700

Length

Max length8
Median length6
Mean length6.0015848
Min length3

Characters and Unicode

Total characters7952700
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000.0
2nd row1000.0
3rd row1000.0
4th row1000.0
5th row1000.0

Common Values

ValueCountFrequency (%)
1000.0 1322300
99.8%
200000.0 2100
 
0.2%
0.0 700
 
0.1%

Length

2023-08-10T18:43:39.554514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T18:43:39.662158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1000.0 1322300
99.8%
200000.0 2100
 
0.2%
0.0 700
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5303200
66.7%
. 1325100
 
16.7%
1 1322300
 
16.6%
2 2100
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6627600
83.3%
Other Punctuation 1325100
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5303200
80.0%
1 1322300
 
20.0%
2 2100
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1325100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7952700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5303200
66.7%
. 1325100
 
16.7%
1 1322300
 
16.6%
2 2100
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7952700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5303200
66.7%
. 1325100
 
16.7%
1 1322300
 
16.6%
2 2100
 
< 0.1%

coupon
Real number (ℝ)

Distinct247
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6221865
Minimum1.55
Maximum8.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:39.760218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.55
5-th percentile2.25
Q12.95
median3.5
Q34.125
95-th percentile5.375
Maximum8.75
Range7.2
Interquartile range (IQR)1.175

Descriptive statistics

Standard deviation0.99403283
Coefficient of variation (CV)0.27442895
Kurtosis2.2077077
Mean3.6221865
Median Absolute Deviation (MAD)0.6
Skewness1.0279602
Sum4799759.3
Variance0.98810127
MonotonicityNot monotonic
2023-08-10T18:43:39.873936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 55300
 
4.2%
4 43400
 
3.3%
3.25 38500
 
2.9%
3 32200
 
2.4%
3.75 32200
 
2.4%
3.375 30100
 
2.3%
3.2 28000
 
2.1%
3.8 27300
 
2.1%
2.75 26600
 
2.0%
3.4 25900
 
2.0%
Other values (237) 985600
74.4%
ValueCountFrequency (%)
1.55 1400
 
0.1%
1.625 1400
 
0.1%
1.65 700
 
0.1%
1.7 4900
0.4%
1.75 1400
 
0.1%
1.8 2800
0.2%
1.85 4900
0.4%
1.875 3500
0.3%
1.9 2100
0.2%
1.902 700
 
0.1%
ValueCountFrequency (%)
8.75 700
 
0.1%
8.375 700
 
0.1%
7.95 700
 
0.1%
7.75 1400
 
0.1%
7.625 2100
0.2%
7.5 3500
0.3%
7.375 2100
0.2%
7.25 700
 
0.1%
7.125 700
 
0.1%
7 1400
 
0.1%

offering_year
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.8452
Minimum2012
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:39.975005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2016
Q32017
95-th percentile2019
Maximum2019
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1006949
Coefficient of variation (CV)0.0010420914
Kurtosis-0.93674086
Mean2015.8452
Median Absolute Deviation (MAD)2
Skewness-0.2821349
Sum2.6711965 × 109
Variance4.4129189
MonotonicityNot monotonic
2023-08-10T18:43:40.056264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2017 252000
19.0%
2016 228200
17.2%
2018 173600
13.1%
2015 149800
11.3%
2019 147000
11.1%
2014 142800
10.8%
2013 116900
8.8%
2012 114800
8.7%
ValueCountFrequency (%)
2012 114800
8.7%
2013 116900
8.8%
2014 142800
10.8%
2015 149800
11.3%
2016 228200
17.2%
2017 252000
19.0%
2018 173600
13.1%
2019 147000
11.1%
ValueCountFrequency (%)
2019 147000
11.1%
2018 173600
13.1%
2017 252000
19.0%
2016 228200
17.2%
2015 149800
11.3%
2014 142800
10.8%
2013 116900
8.8%
2012 114800
8.7%

offering_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3454834
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:40.150230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3376678
Coefficient of variation (CV)0.5259911
Kurtosis-1.2554025
Mean6.3454834
Median Absolute Deviation (MAD)3
Skewness0.036246801
Sum8408400
Variance11.140026
MonotonicityNot monotonic
2023-08-10T18:43:40.236063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 151900
11.5%
3 151900
11.5%
5 147700
11.1%
9 142800
10.8%
2 129500
9.8%
8 115500
8.7%
6 102900
7.8%
1 84700
6.4%
7 84000
6.3%
10 81900
6.2%
Other values (2) 132300
10.0%
ValueCountFrequency (%)
1 84700
6.4%
2 129500
9.8%
3 151900
11.5%
4 77700
5.9%
5 147700
11.1%
6 102900
7.8%
7 84000
6.3%
8 115500
8.7%
9 142800
10.8%
10 81900
6.2%
ValueCountFrequency (%)
12 54600
 
4.1%
11 151900
11.5%
10 81900
6.2%
9 142800
10.8%
8 115500
8.7%
7 84000
6.3%
6 102900
7.8%
5 147700
11.1%
4 77700
5.9%
3 151900
11.5%

maturity_year
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2024.859
Minimum2019
Maximum2039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:40.330097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2019
5-th percentile2021
Q12022
median2024
Q32027
95-th percentile2029
Maximum2039
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.42016
Coefficient of variation (CV)0.0016890856
Kurtosis3.3712725
Mean2024.859
Median Absolute Deviation (MAD)2
Skewness1.4922031
Sum2.6831406 × 109
Variance11.697495
MonotonicityNot monotonic
2023-08-10T18:43:40.426352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2023 194600
14.7%
2022 192500
14.5%
2024 180600
13.6%
2026 137900
10.4%
2027 123900
9.4%
2025 122500
9.2%
2021 91000
6.9%
2028 90300
6.8%
2029 72100
 
5.4%
2020 51800
 
3.9%
Other values (11) 67900
 
5.1%
ValueCountFrequency (%)
2019 4200
 
0.3%
2020 51800
 
3.9%
2021 91000
6.9%
2022 192500
14.5%
2023 194600
14.7%
2024 180600
13.6%
2025 122500
9.2%
2026 137900
10.4%
2027 123900
9.4%
2028 90300
6.8%
ValueCountFrequency (%)
2039 6300
0.5%
2038 11900
0.9%
2037 4900
 
0.4%
2036 7700
0.6%
2035 12600
1.0%
2034 9800
0.7%
2033 2800
 
0.2%
2032 700
 
0.1%
2031 700
 
0.1%
2030 6300
0.5%

maturity_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1674591
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.2 MiB
2023-08-10T18:43:40.526563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3755852
Coefficient of variation (CV)0.54732187
Kurtosis-1.1993941
Mean6.1674591
Median Absolute Deviation (MAD)3
Skewness0.18354206
Sum8172500
Variance11.394576
MonotonicityNot monotonic
2023-08-10T18:43:40.608565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 198100
14.9%
5 127400
9.6%
6 118300
8.9%
11 118300
8.9%
8 109200
8.2%
9 102900
7.8%
4 101500
7.7%
2 100800
7.6%
1 98000
7.4%
10 84700
6.4%
Other values (2) 165900
12.5%
ValueCountFrequency (%)
1 98000
7.4%
2 100800
7.6%
3 198100
14.9%
4 101500
7.7%
5 127400
9.6%
6 118300
8.9%
7 82600
6.2%
8 109200
8.2%
9 102900
7.8%
10 84700
6.4%
ValueCountFrequency (%)
12 83300
6.3%
11 118300
8.9%
10 84700
6.4%
9 102900
7.8%
8 109200
8.2%
7 82600
6.2%
6 118300
8.9%
5 127400
9.6%
4 101500
7.7%
3 198100
14.9%

Interactions

2023-08-10T18:43:31.317725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:14.081330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:15.857720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:17.587500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:19.183746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.973072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.667379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.385873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:26.095902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.954442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:29.571857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:31.473268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:14.241274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.006604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:17.742674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:19.333501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:21.130215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.826489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.545318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:26.436417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.100978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:29.733548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:31.630278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:14.394729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.162684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:17.885778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:19.482822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:21.288359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.986125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.701416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:26.589577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.248615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:29.894649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:31.775415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:14.537124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.312666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.020421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:19.618740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:21.435768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:23.135392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.848223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:26.733347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.385375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.043885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:31.927182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:14.686379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.472684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.162462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:19.762766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:21.584482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:23.289672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.999941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:26.883992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.531232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.200267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:32.075784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:14.937703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.628525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.302007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.049921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:21.731225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:23.441168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:25.158064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.031006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.674770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.354880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:32.232237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:15.092829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.792542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.451483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.207872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:21.889787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:23.594423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:25.316177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.186763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.823362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.518714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:32.386835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:15.245686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:16.951877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.599795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.363943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.046139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:23.755614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:25.470322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.335773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:28.971256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.680692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:32.545732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:15.400571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:17.115431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.749578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.517776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.205591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:23.915889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:25.632873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.489175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:29.125051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.843429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:32.694914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:15.546586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:17.269494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:18.890189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.661839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.354820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.067908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:25.788644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.637451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:29.264395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:30.997284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:32.856432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:15.707569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:17.435003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:19.042024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:20.823494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:22.516393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:24.232682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:25.947669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:27.802237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:29.424667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-10T18:43:31.157655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-10T18:43:40.695064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
entrd_vol_qtrptd_pryld_ptoffering_amtoffering_priceoffering_yieldcouponoffering_yearoffering_monthmaturity_yearmaturity_monthprincipal_amt
entrd_vol_qt1.000-0.0140.0550.3340.0570.0020.0040.150-0.0480.044-0.0570.000
rptd_pr-0.0141.000-0.5060.103-0.0550.3440.3500.143-0.0580.411-0.0230.008
yld_pt0.055-0.5061.000-0.1660.0030.3660.3650.087-0.0000.245-0.0100.000
offering_amt0.3340.103-0.1661.0000.095-0.179-0.1740.119-0.0970.006-0.1010.041
offering_price0.057-0.0550.0030.0951.000-0.036-0.0030.123-0.026-0.139-0.0530.030
offering_yield0.0020.3440.366-0.179-0.0361.0000.9890.036-0.0530.418-0.0220.000
coupon0.0040.3500.365-0.174-0.0030.9891.0000.035-0.0520.420-0.0250.038
offering_year0.1500.1430.0870.1190.1230.0360.0351.000-0.1910.489-0.1660.067
offering_month-0.048-0.058-0.000-0.097-0.026-0.053-0.052-0.1911.000-0.0920.7050.104
maturity_year0.0440.4110.2450.006-0.1390.4180.4200.489-0.0921.000-0.1020.045
maturity_month-0.057-0.023-0.010-0.101-0.053-0.022-0.025-0.1660.705-0.1021.0000.083
principal_amt0.0000.0080.0000.0410.0300.0000.0380.0670.1040.0450.0831.000

Missing values

2023-08-10T18:43:33.316611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-10T18:43:34.441917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cusip_idtrd_exctn_dtbond_sym_idcompany_symbolentrd_vol_qtrptd_pryld_ptoffering_amtoffering_priceoffering_yieldprincipal_amtcouponoffering_yearoffering_monthmaturity_yearmaturity_month
000037BAB82019-03-20ABB3852125ABB75000.099.962002.8871331250000.097.8333.129051000.02.8752012520225
100037BAB82019-03-21ABB3852125ABB1305000.0100.369002.7501511250000.097.8333.129051000.02.8752012520225
200037BAB82019-03-22ABB3852125ABB1170000.0100.307002.7709051250000.097.8333.129051000.02.8752012520225
300037BAB82019-03-25ABB3852125ABB600000.0100.786262.6099051250000.097.8333.129051000.02.8752012520225
400037BAB82019-03-26ABB3852125ABB100000.0100.669002.6490101250000.097.8333.129051000.02.8752012520225
500037BAB82019-03-27ABB3852125ABB100000.0101.110002.5010971250000.097.8333.129051000.02.8752012520225
600037BAB82019-03-28ABB3852125ABB1535000.0100.777002.6121481250000.097.8333.129051000.02.8752012520225
700037BAB82019-03-29ABB3852125ABB1459000.0100.768002.6149601250000.097.8333.129051000.02.8752012520225
800037BAB82019-04-01ABB3852125ABB32000.0100.509002.7020671250000.097.8333.129051000.02.8752012520225
900037BAB82019-04-02ABB3852125ABB360000.0100.784002.6091451250000.097.8333.129051000.02.8752012520225
cusip_idtrd_exctn_dtbond_sym_idcompany_symbolentrd_vol_qtrptd_pryld_ptoffering_amtoffering_priceoffering_yieldprincipal_amtcouponoffering_yearoffering_monthmaturity_yearmaturity_month
134539098978VAN32022-08-18PFE4666688PFE50000.099.690003.958561500000.099.8113.923031000.03.92018820288
134539198978VAN32022-08-22PFE4666688PFE29000.098.478004.189910500000.099.8113.923031000.03.92018820288
134539298978VAN32022-08-23PFE4666688PFE200000.098.239004.236037500000.099.8113.923031000.03.92018820288
134539398978VAN32022-08-24PFE4666688PFE1000000.098.562004.173956500000.099.8113.923031000.03.92018820288
134539498978VAN32022-08-25PFE4666688PFE525000.098.392054.206974500000.099.8113.923031000.03.92018820288
134539598978VAN32022-08-26PFE4666688PFE210000.098.387004.208056500000.099.8113.923031000.03.92018820288
134539698978VAN32022-08-29PFE4666688PFE1500000.098.036004.276044500000.099.8113.923031000.03.92018820288
134539798978VAN32022-08-30PFE4666688PFE125000.098.041004.275075500000.099.8113.923031000.03.92018820288
134539898978VAN32022-08-31PFE4666688PFE480000.097.872004.308006500000.099.8113.923031000.03.92018820288
134539998978VAN32022-09-01PFE4666688PFE400000.097.148004.450082500000.099.8113.923031000.03.92018820288